Microsoft 070-778 Korean Exam Details & Actual Exam Questions

Exam Code/Number: 070-778 Korean
Exam Name/Title: Analyzing and Visualizing Data with Power BI (070-778 Korean Version)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Power BI

Exam Questions: 155
Updated On: Jul,10 2026
Certification Level: Associate

Microsoft Analyzing and Visualizing Data with Power BI (070-778 Korean Version) Exam Questions

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Microsoft 070-778 Korean Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Analyzing and Visualizing Data with Microsoft Power BI
Exam Number:	70-778
Related Certifications:	MCSA: BI Reporting (requires 70-778 and other exams)
Real Exam Qty:	40-60
Exam Format:	Multiple Choice, Short Answer, Drag and Drop
Exam Price:	USD 165
Certificate Validity Period:	Retired (Exam retired January 31, 2021; superseded by DA-100 / PL-300)
Available Languages:	English
Exam Duration:	120 minutes
Passing Score:	700 (on a scale of 1000)
Sample Questions:	Microsoft 070-778 Korean Sample Questions
Exam Way:	Delivered via Pearson VUE testing or online proctored; retired as of January 31, 2021, replaced by role-based exam (e.g., DA-100 / PL-300).
Pre Condition:	Recommended experience with Power BI Desktop and Power BI Service, data analysis and visualization tasks.
Official Syllabus URL:	https://learn.microsoft.com/

Microsoft 070-778 Korean Exam Syllabus Topics:

Section

Objectives

Model and Visualize Data

- Data Modeling and DAX

1. Optimize data models
2. Manage relationships and hierarchies
3. Create calculated tables/columns and measures

- Report and Visualization Design

1. Create and format interactive visuals
2. KPI and performance visualization
3. Configure report layout and interaction

Configure Dashboards, Reports and Apps

- Power BI Service Integration

1. Security and access configuration
2. Configure dashboards and apps
3. Publish and embed reports

Consume and Transform Data

- Connect to Data Sources

1. Connecting to databases and files
2. Using Power Query for transformation
3. Cleansing and shaping data